Exploring Deep Learning for Video Understanding

Video analysis and understanding have obtained more and more attention in recent years. The research community also has devoted considerable effort and made progress in many related visual tasks, like video action/event recognition, thumbnail frame or video index retrieval, and zero-shot learning. The way to find good representative features of videos is an important objective for these visual tasks.

Thanks to the success of deep neural networks in recent vision tasks, it is natural to take the deep learning methods into consideration for better extraction of a global representation of the images and videos. In general, Convolutional Neural Network (CNN) is utilized for obtaining the spatial information, and Recurrent Neural Network (RNN) is leveraged for capturing the temporal information.

This dissertation provides a perspective of the challenging problems in different kinds of videos which may require different solutions. Therefore, several novel deep learning-based approaches of obtaining representative features are outlined for different visual tasks like zero-shot learning, video retrieval, and video event recognition in this dissertation. To better understand and obtained the video spatial and temporal information, Convolutional Neural Network and Recurrent Neural Network are jointly utilized in most approaches. And different experiments are conducted to present the importance and effectiveness of good representative features for obtaining a better knowledge of video clips in the computer vision field. This dissertation also concludes a discussion with possible future works of obtaining better representative features of more challenging video clips.